Multi-column deep neural networks for image classification
| Autor: | Jürgen Schmidhuber, Dan Ciresan, Ueli Meier |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Artificial Intelligence Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Convolutional neural network 0202 electrical engineering electronic engineering information engineering medicine Traffic sign recognition Artificial neural network Contextual image classification business.industry Deep learning 020207 software engineering Pattern recognition Artificial Intelligence (cs.AI) Visual cortex medicine.anatomical_structure Receptive field 020201 artificial intelligence & image processing Artificial intelligence business computer MNIST database |
| Zdroj: | 2012 IEEE Conference on Computer Vision and Pattern Recognition. |
| Popis: | Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks. 20 pages, 14 figures, 8 tables |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|